class_docstr: Wide Resnet with the softmax layer chopped off
class_docstr_tok: ["Wide","Resnet","with","the","softmax","layer","chopped","off"]
code: def _wide_layer(
      self, block, planes, num_blocks, dropout_rate, stride
  ):
      strides = [stride] + [1] * (int(num_blocks) - 1)
      layers = []
  
      for stride in strides:
          layers.append(
              block(self.in_planes, planes, dropout_rate, stride)
          )
          self.in_planes = planes
  
      return nn.Sequential(*layers)
  
file_path: generalized-moe/domainbed/lib/wide_resnet.py
imports: import sys
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.nn.init
  from torch.autograd import Variable
label_desc: Use image node_id 2 for calling the Wide_ResNet obj's underlying member method code with example usage: obj._wide_layer(block, planes, num_blocks, dropout_rate, stride) and returns: nn